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AutoNLU: Detecting, root-causing, and fixing NLU model errors
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering.
EviNets: Neural Networks for Combining Evidence Signals for Factoid Question Answering
A critical task for question answering is the final answer selection stage, which has to combine multiple signals available about each answer candidate.
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